Introduction
The Earth system is a complex interplay of physical, chemical, and biological
processes that interact in nonlinear ways (Ladyman et al., 2013; Lorenz,
1963; Rind, 1999; Williams, 2005). Much effort has been devoted to
understanding these complex interactions, and several improvements have been
made since the end of the last century.
One of the most important advances in this field has been the use of coupled
numerical climate models, dubbed Earth system models or ESMs (Edwards,
2011; Flato, 2011; Schellnhuber, 1999). These models aim to simulate the
complex interactions of the atmosphere, ocean, land surface, and cryosphere,
together with the carbon and nitrogen cycles (Giorgetta et al., 2013;
Hurrell et al., 2013; Martin et al., 2011; Schmidt et al., 2014).
However powerful, climate models are far from being perfect (Hargreaves,
2010; Hargreaves and Annan, 2014). Unresolved processes (Williams, 2005),
limited computational resources (Shukla et al., 2010; Washington et al.,
2009), and model uncertainties (Baumberger et al., 2017; Lahsen, 2005;
Steven and Bony, 2013) are ongoing issues that still require attention and
further improvement.
One of the fields most in need of development is the inclusion of
co-evolutionary dynamical interactions of the socioeconomic dimension into
global models with other Earth system components (Nobre et al., 2010;
Robinson et al., 2018; Sarofim and Reilly, 2011). Human activity has become
a major driver of change in the Earth system, especially over the past
several decades (Alter et al., 2017; Barnett et al., 2008; Crutzen, 2002),
and it now dominates the natural system in many different ways (Motesharrei
et al., 2016; Ruth et al., 2011). However, most global models use basic
socioeconomic assumptions about the behavior of societies and are only
unidirectionally linked to the biogeophysical part of the Earth system
(Müller-Hansen et al., 2017; Smith et al., 2014). The standard way of
introducing anthropogenic climate change into ESMs is through representative
concentration pathways (RCPs). These are consistent sets of projections
involving only radiative forcing components (van Vuuren et al., 2011), but
which represent a step forward from the scenario approach of the last decade
(Moss et al., 2010; van Vuuren et al., 2014; van Vuuren and Carter, 2014).
However, RCPs are not fully integrated socioeconomic parameterizations but
rather estimates for describing plausible trajectories of human climate
change drivers (Moss et al., 2010; van Vuuren et al., 2012). They provide
simplified accounts of human activities and processes from one-way coupled
integrated assessment models (IAMs; Müller-Hansen et al., 2017).
The use of RCPs is advantageous because they provide a set of pathways that
serve to initialize climate models. However, two major problems remain
within this approach. Firstly, human activities are not intrinsically
embedded into the ESM, impeding sensitivity studies. Secondly, because of
the weak coupling of IAMs, they cannot capture the sometimes
counterintuitive bidirectional feedback and nonlinearity between the
socioeconomic and natural subsystems (Motesharrei et al., 2016; Ruth et al.,
2011). Good examples that illustrate the importance of including such
bidirectional feedbacks feature in the HANDY model (Motesharrei et al., 2014)
which has been used to analyze the key mechanisms behind societal collapses.
The RCP approach has been used in climate models because of its low
computational cost. However, advances in computational resources now allow
to parameterize human–Earth processes in a more detailed way, including the
inclusion of population dynamics into the modeling, as in the POPEM
(POpulation Parameterization for Earth
Models) module (Navarro et al., 2017).
One important, but sometimes overlooked, process is the direct regional
effect of anthropogenic greenhouse gas (GHG) emissions. Although some GHGs
quickly mix in the atmosphere (IPCC, 2014a), their mixing times and
lifetimes vary (Archer et al., 2009; Prather, 2007), and localized emissions
may produce a transient response in the atmosphere. Given the highly
nonlinear character of the processes involved, it is not unreasonable to
assume that accounting for geographical variability is significant, and the
spatial and time distribution of these emissions may affect global climate
(Alter et al., 2017; Grandey et al., 2016; Guo et al., 2013). This
hypothesis has seldom been investigated, as most current models treat
certain GHG emissions as a homogeneously distributed forcing. Thus, for
instance, the most typical CESM (Community Earth System Model) simulations
prescribe a CO2 concentration on the assumption that it is well mixed
in the atmosphere (Neale et al., 2012).
This paper describes the results of a 50-year simulation with a simple
parameterization of fossil fuel CO2 emissions at model grid-point
scale, integrating the POPEM module into the CESM. The aim of this paper is
to show that this grid-point scale modeling of anthropogenic CO2
emissions (and other pollutants) represents an improvement over simpler
approaches, and leads to better representation of the geographical
variability of precipitation.
The purpose of the new modeling is not only to improve precipitation and
temperature estimates but also to help understand the carbon cycle feedback,
and evaluate the climate sensitivity of the Earth under alternative GHG
emission scenarios. While our focus here is anthropogenic CO2
emissions, the POPEM parameterization can accommodate other GHGs and
human-dependent processes in order to advance CESMs towards a comprehensive
fully coupled modeling of anthropogenic dynamics in the global climate.
The paper is organized as follows: in Sect. 2, we present the validation
of the POPEM stand-alone mode and set the framework for evaluating the impact
of POPEM parameterization – its incorporation into the CESM and the testing
framework; in Sect. 3, we compare the outputs of CONTROL and POPEM runs
and see how they compare with observations. In Sect. 4, we highlight the importance of the dynamical modeling of
anthropogenic emissions at grid-point scale to better represent the
socioeconomic parameters in the CESM model and improve precipitation estimates.
Material and methods
The CESM model
The Community Earth System Model (CESM) is a state-of-the-art ESM and
probably the most widely used climate model. It was developed and is
maintained by the National Center for Atmospheric Research (NCAR), with
contributions from external researchers funded by the US Department of
Energy, the National Aeronautics and Space Administration (NASA), and
the National Science Foundation (Hurrell et al., 2013). CESM is an ESM
comprising a system of multi-geophysical components, which periodically
exchange two-dimensional boundary data in the coupler (Craig et al., 2012).
It consists of five component models and one central coupler component: the
atmosphere model CAM (Community Atmosphere Model; Tilmes et al., 2015); the
ocean model POP (Parallel Ocean Program; Kerbyson and Jones, 2005); the
land model CLM (Community Land Model; Lawrence et al., 2011); the sea ice
model CICE (Community Ice Code; Hunke and Lipscomb, 2008); and the ice
sheet model CISM (Community Ice Sheet Model; Lipscomb et al., 2013).
CESM – formerly the Community Climate System Model (CCSM) – was conceived as
a coupled atmospheric–oceanic circulation model (Boville and Gent, 1998;
Collins et al., 2006; Gent et al., 2011; Hurrell et al., 2013; Williamson,
1983). Since the release of the first version, CESM has evolved into a
complex Earth system model now used in different fields. This includes
research into atmospheric (Bacmeister et al., 2014; Liu et al., 2012; Yuan
et al., 2013), biogeochemical (Lehner et al., 2015; Nevison et al., 2016;
Val Martin et al., 2014), and human-induced processes (Huang and Ullrich,
2016; Levis et al., 2012; Oleson et al., 2011), as well as others. The core
code of CESM has also been utilized by various research centers for
developing their own models (norESM; Bentsen et al., 2013; CMCC–CESM–NEMO; Fogli
and Iovino, 2014; MIT IGSM-CAM; Monier et al., 2013). CESM has been used in
many hundreds of peer-reviewed studies to better understand climate
variability and climate change (Hurrell et al., 2013; Kay et al., 2015;
Sanderson et al., 2017). Simulations performed with CESM have made a
significant contribution to international assessments of climate, including
those of the Intergovernmental Panel on Climate Change (IPCC) and the
CMIP5/6 project (Coupled Model Intercomparison Project Phase 5/6) (Eyring et
al., 2016; IPCC, 2014b; Taylor et al., 2012).
A major advantage of CESM over other ESMs is its availability. Some climate
models are developed by scientific groups and access to the source code is
limited. The CESM source code is free and available to download from the
NCAR website. This approach helps improve the model by setting up a
framework for collaborative research and makes the model fully auditable.
CESM is a good example of a “full confidence level” model, after Tapiador et
al. (2017), where many “avatars” of the code are routinely run in several
independent research centers, and there is an entire community improving the
model and reporting on issues and results. However, the model is not immune
to bias. One important shortcoming is the poor representation of
precipitation in terms of spatial structure, intensity, duration, and
frequency (Dai, 2006; Tapiador et al., 2018; Trenberth et al., 2015, 2017).
Another major bias is the anomalous warm surface
temperature in coastal upwelling regions
(Davey et al., 2002; Justin Small et al.,
2015; Richter, 2015).
POPEM specifics and stand-alone validation
POPEM parameterization model overview
The POPEM module is a demographic projection model coded in FORTRAN that is
intended to estimate monthly fossil fuel CO2 emissions at model grid-point scale
using population as the input. Due to a lack of actual GHG
measurements at appropriate spatial and temporal scales, it is necessary to
use a proxy. For this, POPEM employs population, the evolution of which is
modeled using external parameters that feed the module. The idea of using
population as proxy is not new, and population density has previously been
used to downscale national CO2 emissions (Andres et al., 1996, 2016).
However, these inventories were not dynamical but instead tied to
historical data so it is not possible to use them either to estimate future
changes in emissions or coupled with other components of the model. This
change represents an important advance in the way emissions are computed.
Thus, POPEM uses a bottom-up approach, where emissions are calculated at
cell level on the basis of population projections, while global inventories
use a top-down approach, which is less flexible when coupled with other
components of the ESM.
The demographic/emissions module presented here is an updated version of the
demographic module explained in Navarro et al. (2017). The differences
between the versions are minimal. They involve better approximation of
emissions in highly polluting regions with poor population data, such as
China; a better estimate for coastal zones and country limits; and a change
in the model time step for more efficient coupling with CESM. The inclusion
of these changes results in more accurate emission estimates when compared
with inventories than the previous version did. However, the model is not
immune to bias. The most important limit is the degradation of the model
outputs when there is increased spatial resolution – resolution of 0.25∘ and higher.
Detailed information on POPEM and its validation in the demographic realm
can be found in Navarro et al. (2017). In short, from an initial condition,
the routine computes the population for each model grid point in a 2-D matrix
and then calculates fossil fuel CO2 emissions using per capita
emission rates by nations. The process is repeated for each time step
(e.g., annually) throughout the simulation period.
As seen in Fig. 1, POPEM stores gridded emission data in a 3-D array (time,
latitude, and longitude) to be used by the modified version of the
co2_cycle module. This module reads emission data and passes this to the
atm_comp_mct, which calculates the total amount of CO2 emissions from different
sources (land, ocean, and fossil fuel).
POPEM trend verification
Prior to coupling POPEM with CESM, we performed several tests to evaluate its
ability to reproduce historical population trends and CO2 emissions. To
do this, we ran the module in stand-alone mode. In a first test, we ran a
short simulation (1950–2013) and compared the emission data with a standard
emissions inventory (CDIAC). In a second test, POPEM was run for 70 years (1950–2020)
and population estimates were validated against the UN (United Nations) population
statistics database for those years when data were available.
Conceptual scheme of the POPEM module coupled with the CAM5
atmosphere module. POPEM requires three input data sets to compute emissions
(black dashed rectangles): initial population distribution; demographic
parameters (age structure, death, and birth rates); and per capita emission
rates by country. POPEM provides a 3-D array (time, latitude, longitude) with
emissions that are read by the co2_cycle module and passed to the
atm_comp_mct module which computes the total amount of
CO2 in the atmosphere.
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As shown in Fig. 2, POPEM is capable of satisfactorily simulating the
observed population. Comparison with UN data shows good agreement. However,
POPEM presents slight differences from the reference data in some regions.
Several of these discrepancies can be explained by the initial model
conditions; POPEM uses the same age distribution inside each grid cell to
initiate the model (only for the first time step). This distribution is
based on the global average age structure. Consequently, the model
overestimates the population in those regions with a more elderly age
structure, i.e., Europe and North America, and underestimates areas with
younger populations, i.e., Latin America and Asia.
These disparities in population counts have a diverse effect on the outputs
in terms of GHG emissions. Thus, for example, the bias in Europe seems to be
more important than the bias in Latin America and Oceania. Two principal
reasons could explain this: population size, as Europe has a larger
population than Oceania, so there is greater bias in the CO2
emissions estimation; and the per capita emissions rate, as Latin American
countries have lower per capita emission rates than European nations.
It is worth noting here that the POPEM outputs in Fig. 2 are clearly
nonlinear and thus not trivially derived from simply extrapolating
population. The North American estimate of CO2 emissions (second row
from the bottom) clearly shows the added value introduced by the model.
Comparison of the population estimates for the years 1950–2020 (a)
and the historical CO2 emission estimates for the years 1950–2012 (b).
The first row compares global data, the second to seventh rows compare regional data
(Africa, Europe, Latin America, North America, and Oceania). In (a),
the red line shows the estimates given using POPEM and blue indicates UN estimates.
Values are given in billions of people. In (b), the red line
shows the estimates given using POPEM and the black indicates CDIAC estimates.
Units are given in million metric tons.
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POPEM CO2 emission estimates for 1950, 1980, and 2000.
POPEM produces a gridded representation of anthropogenic CO2 emissions
using population dynamics and country per capita emissions derived from the
CDIAC database. Values are given in millions of metric tons per year.
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Figure 3 shows how POPEM distributes CO2 emissions for different years
in the recent past. In 1950, the majority of emissions tended to be
concentrated in the USA and Europe, while in 2000, China, the USA, and India
were the most polluting countries. This is consistent with the literature:
POPEM's estimates generally agree with the emissions maps for the recent
past (Andres et al., 1996; Boden et al., 2017; Oda et al., 2018; Rayner et
al., 2010), as well as with regional studies on CO2 emissions (Gately
et al., 2013; Gurney et al., 2009).
The regionalized distribution of emissions depicted in Fig. 3 represents a
vast improvement over the standard procedure of using globally averaged
emissions. Even accounting for rapid mixing of GHGs, transient effects
are likely to appear given the hemispheric contrast and regional differences
in the emissions. The differences in Asia are illustrative of the economic
changes in the recent past and the exponential pace of industrialization in that region.
CESM experimental setup
The CESM used in this work is based on version 1.2.2
(http://www.cesm.ucar.edu/models/, last access: 10 February 2018). This set includes active components for
the atmosphere, land, ocean, and sea ice, all coupled by a flux coupler. The
latest atmospheric module CAM5 (Neale et al., 2012) is used to introduce
more accurate modeling of atmospheric physics. Additionally, the carbon
cycle module is included in CESM's atmosphere, land, and ocean components
(Lindsay et al., 2014).
We ran an experiment at 1.9∘ of spatial resolution for the
period 1950–2000. Two simulations were performed to analyze the effects of
the regionalized emissions (Fig. 3) on the CESM. Our control case used
homogeneous CO2 concentration parameters (standard procedure in ESMs),
while the POPEM case used geographically distributed CO2 emission
data. In the latter, the POPEM module was coupled with the atmospheric
CO2 flux routine to provide monthly gridded CO2 emissions. The
gridded data were used at each time step by the atmospheric routine. Apart
from this change, both simulations were identical in order to identify the
effects (if any) of the POPEM parameterization.
Validation data
GPCP data set
Precipitation is one of the key elements for balancing the energy budget,
and one of the most challenging aspects of climate modeling. Hence, high-quality estimates
of precipitation distribution, amount, and intensity are
essential (Hou et al., 2014; Kidd et al., 2017; Xie and Arkin, 1997). While
there are many sources of precipitation data to be used as a reference (see
Tapiador et al., 2012, for a review), only a few qualify as “full
confidence level validation data” (Tapiador et al., 2017).
The Global Precipitation Climatology Project (GPCP; Adler et al., 2016) has
several products suitable for validating climate models. GPCP-Monthly is one
of the most popular precipitation data sets for climate variability studies.
It combines data from rain gauge stations and satellite observations to
estimate monthly rainfall on a 2.5∘ global grid from 1979 to the
present. The careful combination of satellite-based rainfall estimates
results in the most complete analysis of rainfall available to date over the
global oceans, and adds necessary spatial detail to rainfall analyses over
land. Due to its relevance and global coverage, it has been widely used for
validating precipitation in climate models (Li and Xie, 2014; Pincus et al.,
2008; Stanfield et al., 2016; Tapiador, 2010).
CRU data set
Global surface temperature data sets are an essential resource for
monitoring and understanding climate variability and climate change. One of
the most commonly used data sets is produced by The Climate Research Unit at
the University of East Anglia (CRU). This group produces a high-resolution
gridded climate data set for land-only areas, the Climate Research Unit
Time-series (CRUTS; Harris et al., 2014). CRUTS contains monthly time series
of 10 climate variables, including surface temperature. The data set is
derived from monthly observations at meteorological stations. Station
anomalies are interpolated into 0.5∘ latitude/longitude grid
cells covering the global land surface and combined with existing
climatology data to obtain absolute monthly values (New et al., 1999, 2000).
It is commonly used in the validation of climate models because of its
confidence levels, together with temporal and spatial coverage, and the fact
it compiles station data from multiple variables from numerous data sources
into a consistent format (Christensen and Boberg, 2012; Hao et al., 2013;
Liu et al., 2014; Nasrollahi et al., 2015).
GISTEMP data set
NASA's GISTEMP (GISS Surface Temperature Analysis) is a global surface
temperature change data set (Hansen and Lebedeff, 1987; see Hansen et al.,
2010, for an updated version). It combines land and ocean surface
temperatures to create monthly temperature anomalies at 2∘×2∘
degrees of spatial resolution. The use of anomalies reduces the estimation
error in those places with incomplete spatial and temporal coverage (Hansen
and Lebedeff, 1987). The anomalies are calculated over a fixed base period (1951–1980)
that makes the anomalies consistent over long periods of time.
The first version was originally conceived only for land areas (Hansen and
Lebedeff, 1987) but in 1996 marine surface temperatures were added (Hansen
et al., 1996). The updated version of GISTEMP includes satellite-observed
night lights to identify stations located in extreme darkness and adjust
temperature trends of urban stations for non-climatic factors (Hansen et al.,
2010). Just like CRUTS, GISTEMP is commonly used to validate climate models
because of its coverage and confidence levels (Baker and Taylor, 2016; Brown
et al., 2015; Neely et al., 2016; Peng-Fei et al., 2015).
Results and discussion
Comparison between the CONTROL and POPEM runs
It is worth stressing that a parameterization which performs well when
tested for the variable it models does not necessarily translate into an
overall improvement of the other variables in the model. An accepted
practice in climate modeling is to tune ESMs by adjusting some parameters to
achieve a better agreement with observations (Hourdin et al., 2017;
Mauritsen et al., 2012). These adjustments to specific targets may, however,
decrease the model's overall performance (Hourdin et al., 2017), and give
poor scores for variables other than those tuned. Thus, for example, if a
model is biased with respect to aerosol concentrations or humidity, then
improved parameterization of cloud formation may worsen the performance of
the model with regard to precipitation (Baumberger et al., 2017). This
mismatch can be caused by model over-specification, or over-tuning.
The first step in evaluating the new parameterization is to compare the
outputs with a control simulation to make sure the new addition does not
negatively interact with the dynamical core or spoil the contributions of
the rest of the parameterizations. Figure 4 shows that this is not the case with the
POPEM parameterization, which does not negatively affect the outputs of
precipitation and temperature. Rather, both variables are now closer to the
observed data than they were in the control run, especially in terms of
reducing the double ITCZ (Intertropical Convergence Zone), which
artificially features in global models (Mechoso et al., 1995; for a recent
analysis of double ITCZ in CMIP5 models see Oueslati and Bellon, 2015).
Figure 4a shows that there is just a slight discrepancy in the absolute
difference in rainfall between the GPCP and CESM simulations (the first and
the third quartiles of the distribution remain between ±0.4 mm day-1).
Grid-point to grid-point comparison between the model and GPCP indicates the
ability of CESM to reproduce the spatial distribution of precipitation. In
both simulations, the CESM exhibits a good correlation coefficient (0.72 R2)
compared with the reference data (Fig. 4b). The results are even
better for temperature (0.88 R2; Fig. 4d).
Direct comparison of aggregated data is a standard procedure for gauging
model abilities. Figure 5 compares two latitude–time graphs for
precipitation (Fig. 5a) and surface temperature (Fig. 5b), both for the CONTROL case and
for the new POPEM parameterization.
It is clear from Figs. 5a and 6a that POPEM does alter the spatial pattern
of precipitation and exerts a definite effect on the climate pattern, as the
module reduces the otherwise exaggerated ITCZ precipitation in the Southern
Hemisphere reported by several authors (Hwang and Frierson, 2013; Li and Xie, 2014).
Disparities in temperature between the CONTROL and POPEM runs are apparent
at high latitudes. In this case, POPEM produces lower temperatures at both
poles, a result which deserves further attention (Figs. 5b and 6b).
There are also important differences in precipitation in the 30∘ N–30∘ S band.
Here POPEM reduces model bias, especially in the Southern Hemisphere and on
the Tibetan Plateau (see Sect. 3.2 for more details). On the other hand,
POPEM departs from the control simulation in the Asia Pacific region between
10∘ N–10∘ S. This result reinforces the double ITCZ bias in this area.
Box plots of CESM simulation bias for precipitation (a) and
temperature (c). (b) Scatter plots comparing the annual mean
precipitation (1980–2000) at every grid point for GPCP and CESM simulations
(POPEM and CONTROL). (d) Scatter plots comparing the annual mean
temperature at every grid point for CRU and CESM simulations (POPEM and CONTROL).
Units are in mm day-1 (precipitation) and in ∘C (temperature).
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These results show that the POPEM parameterization generally agrees with
historical data for population, and also compares well with the control
simulation in the sense of addressing some of the known biases in
precipitation and temperature, offering a more detailed version of CO2
emissions at a relatively cheap computational cost. As discussed above, the
CONTROL run uses global concentration values to include CO2 on the
assumption that it is well mixed in the atmosphere (Neale et al., 2012).
This assumption reduces the computational burden of the simulation but does
not allow for precise emissions modeling in the future. This is an important
aspect for regionalized emission scenarios, since even if the new
parameterization is not significantly better than the old approach (but no
worse), it is desirable as it allows for sensitivity analyses, such as
evaluating the effects of the US leaving the Paris Agreement.
Potential applications of POPEM include not only sensitivity analyses of
local CO2 emissions policies but also the added feature of performing
tests for “what if” scenarios. One interesting example would be the climate
response under the hypothesis that China and India – the most populated
countries in the world – reach US CO2 per capita emission rates.
Another “what if” scenario would be the climate response of an increasingly
urbanized world. In both cases, POPEM provides a flexible framework for
testing the alternative hypotheses.
The realism of the ESM will be enhanced with a fully coupled system. Such a
fully fledged ESM will include bidirectional feedback between POPEM and CESM
to evaluate the effects of climate change on population dynamics and emissions.
Validation against observational data sets
Once it has been verified that the new parameterization does not worsen the
modeling, the next step in evaluating the performances is comparing the
simulation outputs for both the CONTROL run and the POPEM module using
actual observational data. Direct comparisons with historical data can help
show whether or not a climate model correctly represents the climate of the
past. However, although observational measurements are often considered the
ground truth to validate models against, it is important to be aware that
measurements have their own uncertainties (Tapiador et al., 2017).
Figure 7 shows a comparison of CESM precipitation simulations for the period 1980–2000
using the GPCP. It is apparent that there is an overall consensus,
even though there are differences. Despite these known biases, the model
agrees with the observations on the major features of global precipitation.
The improvements in parameterizing emissions become clearer if we focus on
specific regions. For the El Niño-4 area, there are
statistically significant differences (at the 0.05 significance level)
between both the CONTROL run and the POPEM modeling when compared with the
reference data. This observation illustrates the limitations of the modeling
and the need of advances in the parameterizations. However, for this area
the correlation (R2) between POPEM and GPCP is slightly better than
CONTROL and GPCP (0.706 R2 versus 0.692 R2).
Latitude vs. time plots for precipitation (a) and surface
temperature (b). For absolute difference graphs, blue represents higher
values in POPEM and red represents higher values in the CONTROL. Units are
in mm day-1 for precipitation and in ∘C for temperature.
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The real added value, however, is not in a better estimation of the totals
but in the ability of POPEM to better capture the structure of the
precipitation. Figure 8 shows the histograms of mean precipitation in the
El Niño-4 area using the POPEM parameterization (Fig. 8a), the standard forcing
approach (CONTROL, Fig. 8b), and the reference GPCP estimates (Fig. 8c). While
the CONTROL simulation severely overestimates the low end of the
distribution, POPEM gives a more realistic value. This result is not
apparent in the otherwise improved correlation of POPEM, and is also buried
in the box plots.
El Niño-4 is important because it presents a lower variance in the SST (sea surface temperature) than any other of the El Niño areas, playing a
key role in identifying El Niño Modoki events (Ashok et al., 2007; Ashok
and Yamagata, 2009; Yeh et al., 2009). The consequences of such events are
severe disruptions in human activities due to the increased risk of
droughts, heat waves, poor air quality, and wildfires (McPhaden et al.,
2006). Thus, precise modeling of the processes in this sector of the Pacific
is extremely important.
Another important benefit of POPEM is the reduction of the double ITCZ bias
in the Southern Hemisphere. Although a small change can be inferred from
Fig. 7a and b, the improvement is buried in the annual mean precipitation
maps. Figure 9a shows that the POPEM results are closer to observations of
the intra-annual variability in precipitation, especially for the driest
months (June–October).
The figure also shows slight improvements for two other typical biases
seen in CESM, namely the excess precipitation in the Tibetan Plateau (Chen
and Frauenfeld, 2014; Su et al., 2013; Fig. 9c) and the bias in some areas
affected by the Asian–Australian monsoon (AAM), such as the top end of Australia
(Meehl and Arblaster, 1998; Meehl et al., 2012; Fig. 9b).
The results for the El Niño-4 area show that detailed, grid-point
emissions of GHGs improve the quantification of precipitation in dry areas,
in agreement with our hypothesis about the benefits of locally distributed
versus global mean forcings. Also, the double ITCZ example shows that the
transient effects of regionalized GHG emissions may even translate into
(long) 50-year climatologies, meaning there is room for improvement in the
“rapidly mixing, well-mixed gases” forcing approach.
Figure 10 compares the annual mean temperatures for the period 1950–2000.
CESM simulations show a significant bias in high latitudes of the Northern
Hemisphere (cf. Fig. 10a and b). In these areas, the model produces
colder temperatures than those registered in the CRUTS reference data but
this is also an issue in the CONTROL run. This deviation is also apparent in
Fig. 4b, where negative values lie away from the idealized regression
line, and indicate further improvement of the CESM.
The bias is also reproduced when compared with temperature anomalies for a
specific region. Thus, for instance, CESM gives poor scores in the Barents
Sea area (Fig. 11a) while POPEM obtains better results for the Bering
Sea, especially in the Russian part (Fig. 11b). Here, POPEM gives
more realistic values for the period 1970–1998 but, even with the
improvement, the model still overestimates the temperature anomaly.
If we focus on global temperature anomalies, CESM simulations are able to
reproduce the progressive increase in the temperature anomaly (Fig. 12a).
However, the CONTROL case simulates a sharp drop at the end of the
period (1990–1999), while POPEM portrays this change as smooth, in agreement
with the observations.
The differences between CONTROL and POPEM are better demonstrated when
comparing land and ocean separately (Fig. 12b and c). While
the temperature anomalies for land are quite similar in both cases, POPEM
provides a better representation of the ocean tendency from 1992 onwards,
and that translates to an overall improvement (Fig. 12a).
A comparison of global annual mean precipitation (1950–2000) for the
CONTROL and POPEM (a). (b) is a comparison of annual mean
surface temperatures. The maps in the right-hand column show the absolute
differences between the simulations (CONTROL minus POPEM). In these, blues
represent higher values in POPEM and reds represent higher values in the CONTROL.
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A comparison of the global annual mean precipitation (1980–2000) as
simulated by the CESM (POPEM and CONTROL) model and GPCP observational database.
(a) Global annual mean precipitation maps for GPCP, POPEM, and CONTROL.
(b) Absolute difference maps. Units are in mm day-1.
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Histograms of the mean precipitation in the El Niño-4 area
(5∘ N–5∘ S, 160∘ E–150∘ W) using the POPEM
parameterization (a), the standard forcing approach (CONTROL, b),
and the reference GPCP estimates (c).
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Validation against ESPI and ONI indices
The El Niño–Southern Oscillation (ENSO) is the most dominant
inter-annual climate variation in the tropics. It occurs when seasonally
averaged SST anomalies in the eastern Pacific Ocean
exceed a given threshold and cause a shift in the atmospheric circulation
(Trenberth, 1997). Historically, the definition of ENSO does not include
precipitation because of the limitations of stations (Ropelewski and
Halpert, 1987), but recent work with satellites has confirmed that this
phenomenon is a major driver of global precipitation variability (Haddad et al., 2004).
Monthly precipitation (1980-1999) based on GPCP, CONTROL, and POPEM for
three of the regions with important biases in CESM. (a) Shows
precipitation for the area affected by the double-ITCZ bias in the Southern
Hemisphere (20∘ S–0 , 80∘ E–100∘ W);
(b) for the top end of Australia (30–10∘ S, 128–140∘ E);
and (c) for the Tibetan Plateau (22–32∘ N, 78–92∘ W).
The black line represents observations (GPCP), the blue line is the CONTROL
case, and the red line is the POPEM case. Units are in mm day-1. The arrow
indicates the improvement of the POPEM model.
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A comparison of the annual mean temperature (1950–2000) as simulated
by the CESM model (POPEM and CONTROL) and CRU observational database.
(a) Global annual mean temperature maps for CRU, POPEM, and CONTROL.
(b) Absolute difference maps. Units are in ∘C.
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A comparison of the annual mean surface temperature anomaly between
GISTEMP, CONTROL, and POPEM from 1950 to 1999. (a) Represents the
Barents Sea (68–80∘ N, 19–68∘ E); (b) Russian part
of the Bering Sea (50–65∘ N, 150–180∘ E); and (c) American
part of the Bering Sea (50–75∘ N, 140–180∘ W). The black
line represents observational data (GISTEMP), the blue line is the CONTROL case,
and the red is the POPEM case. Anomaly was referenced to 1951–1980 period.
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A major advantage of satellite-derived precipitation indices over more
conventional ones is the description of the strength and position of the
Walker circulation (Curtis and Adler, 2000). Under that assumption, Curtis
and Adler (2000) derived three satellite-based precipitation indices: the
ENSO precipitation index (ESPI), El Niño index (EI), and La Niña
index (LI). Precipitation anomalies are averaged over areas of the
equatorial Pacific and Maritime Continent – where the strongest precipitation
anomalies associated with ENSO are found – to construct differences or
basin-wide gradients (Curtis, 2008).
Figure 13 shows a comparison of GPCP, CONTROL, and POPEM for the ESPI, EI,
and LI indices.
Unfortunately, CONTROL and POPEM cases have difficulty simulating the
precipitation patterns associated with ENSO. Figure 13 shows that bias
increases in 1982–1983 and 1997–1998 El Niño years. The same bias emerges when
comparing the EI and LI indices. In that case, the CESM model produces
stronger El Niño/La Niña events than the observed data.
Consequently, we can consider that CESM is unable to obtain a precise
estimate of precipitation patterns, suggesting that current climate models
are far from generating realistic simulations of the precipitation field (Dai, 2006).
Comparison of the ONI index for the period 1950–1999. The table
compares the ability of the models to reproduce the number, strength, and
duration of El Niño events.
Source
Number
Agreement1
Disagreement2
Intensity
Durationavg4
of events
biasavg3
CPC
14
10.3
CONTROL
7
33
121
0.59 ∘C
19.4
POPEM
10
37
121
0.22 ∘C
11.4
1 The number of months that CPC and CESM agree on El Niño.
2 Disagreement defined as the number of months where CPC and CESM obtain
opposite results. 3 Intensity: (|CESM ONI|-|CPC ONI|)/number of cases
(units in ∘C). 4 Mean duration of El Niño event (in months).
A comparison of the global annual mean surface temperature anomaly
between GISTEMP, CONTROL, and POPEM from 1950 to 1999. (a) Global,
(b) land, and (c) ocean. The black line represents observational
data (GISTEMP), the blue line is the CONTROL case, and the red is the POPEM case.
Anomaly was referenced to 1951–1980 period.
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Another widely used ENSO index is the Oceanic Niño Index (hereafter
ONI). ONI was developed by the NOAA Climate Prediction Center (CPC) as the
principal means for monitoring, assessing, and predicting ENSO (Kousky and
Higgins, 2007). This index is defined as 3-month running-mean values of SST
departures from the average in the Niño-3.4 region. It is computed from
a set of homogeneous historical SST analyses (Kousky and Higgins, 2007; Smith et al., 2003).
Time series of precipitation anomalies for the ENSO region after
Curtis and Adler (2000). (a) ENSO precipitation index (ESPI),
(b) El Niño Index (EI), and (c) La Niña Index (LI).
The Black line shows GPCP data, the blue line is the CONTROL case, and the red
line is the POPEM case. Orange shading denotes El Niño years defined as
consecutive months (minimum 3) with NIÑO3.4 SST
anomalies (5∘ N–5∘ S, 170–120∘ W) greater than
+0.5 ∘C.
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Figure 14 compares the ONI index for CPC, POPEM, and CONTROL cases. It is
clear from the figure that POPEM produces a more realistic representation
of the ENSO, especially if we focus on the 1992–1999 period. POPEM also
obtains better results than CONTROL in the number of simulated El Niño
events (see Table 1). The improvement is also noticeable in the intensity.
The CONTROL case exhibits an overly strong ENSO – a common bias in CESM (Tang
et al., 2016) – but POPEM reduces this bias (0.22 ∘C versus
0.59 ∘C).
Another important indicator is the mean duration of El Niño events. Table 1
shows that POPEM obtains better results according to observations (11 months
in CPC, 10 months in POPEM, and 19 months in CONTROL).
Comparison of the Oceanic El Niño Index (ONI) for CPC (a),
POPEM (b), and CONTROL (c) cases. El Niño and La Niña
are defined according to Kousky and Higgins (2007): 3-month running mean with
anomalies greater than +0.5 ∘C (or -0.5 ∘C) for at least
5 consecutive months in the NIÑO3.4 region. The base period for computing
SST departures is 1971–1999.
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Conclusions and future work
Like all models, climate models are simplified versions of the real world
and therefore do not include the full complexity of the Earth system. Due to
certain limitations, e.g., computational resources or spatial and temporal
resolution, climate models have to make assumptions and resort to
parameterizations.
One important simplification is to use prescribed forcings instead of
dynamically modeling GHG emissions. However, precise modeling of
anthropogenic CO2 emissions is important for climate change research as
it allows sensitivity analyses to be performed.
Here we present a new module of gridded CO2 emissions that is coupled
with CESM. The module, denominated POPEM, computes anthropogenic CO2
emissions by using population estimates as a proxy for disaggregating
emissions beyond the national level. POPEM makes CESM use dynamical
emission data instead of fixed concentration parameters.
In terms of population and emissions, the module compares well when
validated with data. Thus, POPEM's estimates for the 1950–2000 period are in
general agreement with population and emission inventories from the recent
past. In spite of the more realistic depiction of the actual emissions
(Fig. 3), issues persist. The performance of the model can be further
improved in places where population projections are difficult to model. For
instance, POPEM tends to underestimate emissions on the west coast of the
United States and the Anatolian Plateau, and overestimates emissions in
China and Japan.
When the POPEM module is coupled with CESM to generate climatologies, the
ability to successfully model precipitation and surface temperature is
preserved. Moreover, the results of 50-year simulations show that the
dynamical modeling of emissions produced by POPEM results in slight but
noticeable differences in the resultant precipitation regime and surface
temperature. Thus, dynamically modeling the emissions alters the ITCZ by
reducing precipitation in the Southern Hemisphere and increasing it in the
Northern Hemisphere. For particularly interesting areas, such as the
El Niño-4 region, the POPEM outperforms the traditional approach.
Further work will be devoted to improving the modeling of those areas and
hopefully minimizing some of the original biases of the CESM model. These
include the emergence of a double ITCZ in CESM simulations, which is a
common bias for most climate models (Oueslati and Bellon, 2015), as well as
SST simulated by climate models, which are generally too low in the Northern
Hemisphere and too high in the Southern Hemisphere (Wang et al., 2014).
Current applications of the parameterization include evaluating the effects
of changes in regional policies, and a better understanding of the carbon
cycle (Friedlingstein et al., 2006). Future work will be devoted to
evaluating the climate response to alternative anthropogenic CO2
emissions, to coupling POPEM with the newest version of CESM (CESM2; Joel,
2018), to fully coupling human–Earth subsystems, to increasing the
spatial resolution of the simulations, and to refining the spatial and
temporal distribution of emission estimates.
Although the version of POPEM presented here is already functional, this
work is intended to be just the first step in fully coupling socioeconomic
dynamics with ESMs. This will include bidirectional feedbacks between human
and Earth systems and the simulation of societal processes based on the
internal dynamics of the model instead of using external sources to make the
projections. Only within a coupled global human–Earth system framework can
we produce more realistic representations of the Earth system capturing much
of the important feedbacks that are missing from current models (Motesharrei
et al., 2016). The success of this approach will depend on the ability of
scientists from different research fields to work in an interdisciplinary
framework of continuous collaboration.